Self-supervised learning has emerged as an efficient approach to representation learning in NLP [1]}, [2]}, [3]}, [4]}, [5]}.
Multi-task learning of pre-trained models has shown improved results on many tasks [6]}, [7]}, including few-shot setting.
[8]} leveraged entailment tasks for few-shot learning.
[9]} developed self-training methods for semi-supervised few-shot learning.
Recently, extremely large language models have been shown to have few-shot capacities [10]}, while
[11]} demonstrated few-shot capacities for small models in the semi-supervised setting. Meanwhile, [12]}, [13]} showed meta-learning to be effective at improving few-shot performance in multi-task and unsupervised settings, as well as improving performance for small models.
